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| # # Test projection of adjusted investment costs | ||
| # def test_project_adjusted_inv_costs(): | ||
| # df_weo = get_weo_data() | ||
| # df_nam_orig_message = get_cost_assumption_data() | ||
| # df_tech_cost_ratios = calculate_region_cost_ratios(df_weo) | ||
|
|
||
| # df_region_diff = get_region_differentiated_costs( | ||
| # df_weo, df_nam_orig_message, df_tech_cost_ratios | ||
| # ) | ||
|
|
||
| # df_learning_rates = get_cost_reduction_data() | ||
| # df_technology_first_year = get_technology_first_year_data() | ||
|
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||
| # df_gdp = get_gdp_data() | ||
| # df_linreg = linearly_regress_tech_cost_vs_gdp_ratios(df_gdp, df_tech_cost_ratios) | ||
|
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||
| # df_adj_cost_ratios = calculate_adjusted_region_cost_ratios(df_gdp, df_linreg) | ||
| # df_nam_learning = project_NAM_inv_costs_using_learning_rates( | ||
| # df_region_diff, df_learning_rates, df_technology_first_year | ||
| # ) | ||
|
|
||
| # res = project_adjusted_inv_costs( | ||
| # df_nam_learning, | ||
| # df_adj_cost_ratios, | ||
| # df_region_diff, | ||
| # convergence_year_flag=2060, | ||
| # ) | ||
|
|
||
| # # Check that the appropriate columns are present | ||
| # assert ( | ||
| # bool( | ||
| # res.columns.isin( | ||
| # [ | ||
| # "scenario", | ||
| # "message_technology", | ||
| # "weo_technology", | ||
| # "r11_region", | ||
| # "year", | ||
| # "inv_cost_learning_only", | ||
| # "inv_cost_gdp_adj", | ||
| # "inv_cost_converge", | ||
| # ] | ||
| # ).any() | ||
| # ) | ||
| # is True | ||
| # ) | ||
|
|
||
| # # Check that the maximum year is 2100 | ||
| # assert res.year.max() == 2100 | ||
|
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||
|
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| # # Test application of polynomial regression | ||
| # def test_apply_polynominal_regression(): | ||
| # df_weo = get_weo_data() | ||
| # df_nam_orig_message = get_cost_assumption_data() | ||
| # df_tech_cost_ratios = calculate_region_cost_ratios(df_weo) | ||
|
|
||
| # df_region_diff = get_region_differentiated_costs( | ||
| # df_weo, df_nam_orig_message, df_tech_cost_ratios | ||
| # ) | ||
|
|
||
| # df_learning_rates = get_cost_reduction_data() | ||
| # df_technology_first_year = get_technology_first_year_data() | ||
|
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||
| # df_gdp = get_gdp_data() | ||
| # df_linreg = linearly_regress_tech_cost_vs_gdp_ratios(df_gdp, df_tech_cost_ratios) | ||
|
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||
| # df_adj_cost_ratios = calculate_adjusted_region_cost_ratios(df_gdp, df_linreg) | ||
| # df_nam_learning = project_NAM_inv_costs_using_learning_rates( | ||
| # df_region_diff, df_learning_rates, df_technology_first_year | ||
| # ) | ||
|
|
||
| # df_adj_inv = project_adjusted_inv_costs( | ||
| # df_nam_learning, | ||
| # df_adj_cost_ratios, | ||
| # df_region_diff, | ||
| # convergence_year_flag=2060, | ||
| # ) | ||
|
|
||
| # res = apply_polynominal_regression(df_adj_inv, convergence_year_flag=2060) | ||
|
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||
| # # Check that the appropriate columns are present | ||
| # assert ( | ||
| # bool( | ||
| # res.columns.isin( | ||
| # [ | ||
| # "scenario", | ||
| # "message_technology", | ||
| # "r11_region", | ||
| # "beta_1", | ||
| # "beta_2", | ||
| # "beta_3", | ||
| # "intercept", | ||
| # ] | ||
| # ).any() | ||
| # ) | ||
| # is True | ||
| # ) | ||
|
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||
|
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||
| # # Test projections using spline regression results | ||
| # def test_apply_splines_projection(): | ||
| # df_weo = get_weo_data() | ||
| # df_nam_orig_message = get_cost_assumption_data() | ||
| # df_tech_cost_ratios = calculate_region_cost_ratios(df_weo) | ||
|
|
||
| # df_region_diff = get_region_differentiated_costs( | ||
| # df_weo, df_nam_orig_message, df_tech_cost_ratios | ||
| # ) | ||
|
|
||
| # df_learning_rates = get_cost_reduction_data() | ||
| # df_technology_first_year = get_technology_first_year_data() | ||
|
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||
| # df_gdp = get_gdp_data() | ||
| # df_linreg = linearly_regress_tech_cost_vs_gdp_ratios(df_gdp, df_tech_cost_ratios) | ||
|
|
||
| # df_adj_cost_ratios = calculate_adjusted_region_cost_ratios(df_gdp, df_linreg) | ||
| # df_nam_learning = project_NAM_inv_costs_using_learning_rates( | ||
| # df_region_diff, df_learning_rates, df_technology_first_year | ||
| # ) | ||
|
|
||
| # df_adj_inv = project_adjusted_inv_costs( | ||
| # df_nam_learning, | ||
| # df_adj_cost_ratios, | ||
| # df_region_diff, | ||
| # convergence_year_flag=2060, | ||
| # ) | ||
|
|
||
| # df_poly_reg = apply_polynominal_regression(df_adj_inv, convergence_year_flag=2060) | ||
|
|
||
| # res = apply_splines_projection( | ||
| # df_region_diff, df_technology_first_year, df_poly_reg, df_adj_inv | ||
| # ) | ||
|
|
||
| # # Check that the appropriate columns are present | ||
| # assert ( | ||
| # bool( | ||
| # res.columns.isin( | ||
| # [ | ||
| # "scenario", | ||
| # "message_technology", | ||
| # "r11_region", | ||
| # "year", | ||
| # "inv_cost_learning_only", | ||
| # "inv_cost_gdp_adj", | ||
| # "inv_cost_converge", | ||
| # "inv_cost_splines", | ||
| # ] | ||
| # ).any() | ||
| # ) | ||
| # is True | ||
| # ) | ||
|
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||
| # # Check that the maximum year is 2100 | ||
| # assert res.year.max() == 2100 | ||
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||
|
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| # # Test function to get final investment and fixed costs | ||
| # def test_project_final_inv_and_fom_costs(): | ||
| # df_weo = get_weo_data() | ||
| # df_nam_orig_message = get_cost_assumption_data() | ||
| # df_tech_cost_ratios = calculate_region_cost_ratios(df_weo) | ||
| # df_fom_inv_ratios = calculate_fom_to_inv_cost_ratios(df_weo) | ||
|
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||
| # df_region_diff = get_region_differentiated_costs( | ||
| # df_weo, df_nam_orig_message, df_tech_cost_ratios | ||
| # ) | ||
|
|
||
| # df_learning_rates = get_cost_reduction_data() | ||
| # df_technology_first_year = get_technology_first_year_data() | ||
|
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||
| # df_gdp = get_gdp_data() | ||
| # df_linreg = linearly_regress_tech_cost_vs_gdp_ratios(df_gdp, df_tech_cost_ratios) | ||
|
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||
| # df_adj_cost_ratios = calculate_adjusted_region_cost_ratios(df_gdp, df_linreg) | ||
| # df_nam_learning = project_NAM_inv_costs_using_learning_rates( | ||
| # df_region_diff, df_learning_rates, df_technology_first_year | ||
| # ) | ||
|
|
||
| # df_adj_inv = project_adjusted_inv_costs( | ||
| # df_nam_learning, | ||
| # df_adj_cost_ratios, | ||
| # df_region_diff, | ||
| # convergence_year_flag=2060, | ||
| # ) | ||
|
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||
| # df_poly_reg = apply_polynominal_regression(df_adj_inv, convergence_year_flag=2060) | ||
|
|
||
| # df_spline_projections = apply_splines_projection( | ||
| # df_region_diff, df_technology_first_year, df_poly_reg, df_adj_inv | ||
| # ) | ||
|
|
||
| # res = project_final_inv_and_fom_costs( | ||
| # df_spline_projections, | ||
| # df_fom_inv_ratios, | ||
| # use_gdp_flag=False, | ||
| # converge_costs_flag=True, | ||
| # ) | ||
|
|
||
| # # Check that the appropriate columns are present | ||
| # assert ( | ||
| # bool( | ||
| # res.columns.isin( | ||
| # [ | ||
| # "scenario", | ||
| # "message_technology", | ||
| # "r11_region", | ||
| # "year", | ||
| # "inv_cost", | ||
| # "fix_cost", | ||
| # ] | ||
| # ).any() | ||
| # ) | ||
| # is True | ||
| # ) | ||
|
|
||
| # # Check that the maximum year is 2100 | ||
| # assert res.year.max() == 2100 | ||
|
|
||
| # # Check that all fix costs are less than investment costs | ||
| # assert bool((res.fix_cost / res.inv_cost).max() < 1) | ||
| import numpy as np | ||
|
|
||
| from message_ix_models.tools.costs.config import FIRST_MODEL_YEAR | ||
| from message_ix_models.tools.costs.learning import ( | ||
| project_ref_region_inv_costs_using_learning_rates, | ||
| ) | ||
| from message_ix_models.tools.costs.splines import apply_splines_to_convergence | ||
| from message_ix_models.tools.costs.weo import get_weo_region_differentiated_costs | ||
|
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||
|
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||
| def test_apply_splines_to_convergence(): | ||
| in_node = "r12" | ||
| in_ref_region = "R12_NAM" | ||
| in_base_year = 2021 | ||
| in_module = "materials" | ||
| in_convergence_year = 2060 | ||
| in_scenario = "SSP2" | ||
|
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||
| df_region_diff = get_weo_region_differentiated_costs( | ||
| input_node=in_node, | ||
| input_ref_region=in_ref_region, | ||
| input_base_year=in_base_year, | ||
| input_module=in_module, | ||
| ) | ||
|
|
||
| df_ref_reg_learning = project_ref_region_inv_costs_using_learning_rates( | ||
| df_region_diff, | ||
| input_node=in_node, | ||
| input_ref_region=in_ref_region, | ||
| input_base_year=in_base_year, | ||
| input_module=in_module, | ||
| ) | ||
|
|
||
| if in_scenario is not None: | ||
| df_ref_reg_learning = df_ref_reg_learning.query("scenario == @sel_scen") | ||
|
|
||
| df_pre_costs = df_region_diff.merge( | ||
| df_ref_reg_learning, on="message_technology" | ||
| ).assign( | ||
| inv_cost_converge=lambda x: np.where( | ||
| x.year <= FIRST_MODEL_YEAR, | ||
| x.reg_cost_base_year, | ||
| np.where( | ||
| x.year < in_convergence_year, | ||
| x.inv_cost_ref_region_learning * x.reg_cost_ratio, | ||
| x.inv_cost_ref_region_learning, | ||
| ), | ||
| ), | ||
| ) | ||
|
|
||
| df_splines = apply_splines_to_convergence( | ||
| df_pre_costs, | ||
| column_name="inv_cost_converge", | ||
| input_convergence_year=in_convergence_year, | ||
| ) | ||
|
|
||
| # Assert that all regions are present | ||
| regions = [ | ||
| "R12_AFR", | ||
| "R12_CHN", | ||
| "R12_EEU", | ||
| "R12_FSU", | ||
| "R12_LAM", | ||
| "R12_MEA", | ||
| "R12_NAM", | ||
| "R12_PAO", | ||
| "R12_PAS", | ||
| "R12_SAS", | ||
| "R12_WEU", | ||
| ] | ||
| assert bool(all(i in df_splines.region.unique() for i in regions)) is True | ||
|
|
||
| # Assert that materials and base technologies are present | ||
| tech = [ | ||
| "coal_ppl", | ||
| "gas_ppl", | ||
| "gas_cc", | ||
| "biomass_NH3", | ||
| "biomass_NH3", | ||
| "furnace_foil_steel", | ||
| ] | ||
| assert bool(all(i in df_splines.message_technology.unique() for i in tech)) is True | ||
|
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||
| # For each region, using coal_ppl as an example, assert that the costs converge | ||
| # to approximately the reference region costs | ||
| # in the convergence year | ||
| for i in regions: | ||
| assert ( | ||
| np.allclose( | ||
| df_splines.query( | ||
| "region == @in_ref_region \ | ||
| and message_technology == 'coal_ppl' \ | ||
| and year >= @in_convergence_year" | ||
| ).inv_cost_splines, | ||
| df_splines.query( | ||
| "region == @i \ | ||
| and message_technology == 'coal_ppl' \ | ||
| and year >= @in_convergence_year" | ||
| ).inv_cost_splines, | ||
| rtol=3, | ||
| ) | ||
| is True | ||
| ) |